In SRGAN [1]}, the generator is the SRResNet and the discriminator uses the architecture proposed by Radford et al. [2]}. In ESRGAN [3]}, Wang et al. made two modifications to the SRResNet: (1) replace the original residual block with the residual-in-residual dense block; (2) remove the BN layers to improve the generalization ability of the model. In SRFeat [4]}, Park et al. indicated that the GAN-based SISR methods tend to produce less meaningful high-frequency noise in reconstructed images. Therefore, they adopted two discriminators: an image discriminator and a feature discriminator, where the latter is trained to distinguish SR images from HR images based on the intermediate feature map extracted from a VGG network. In ESRGAN [3]}, Wang et al. adopted the Relativistic GAN [6]}, where the standard discriminator was replaced with the relativistic average discriminator to learn the relatively realistic between two images. This modification helps the generator to learn sharper edges and more detailed textures.
